"the strongest evidence to date" on effect of "97% consensus" messaging - Cultural Cognition of Health

There’s a new study out on effect of “97% consensus” messaging.

Actually, it is a new analysis of data that were featured in an article published a few months ago in Climatic Change .

The earlier paper reported that after being told that 97% of scientists accept human-caused climate change, study subjects increased their estimate of the percentage of scientists who accept human-caused climate change.

The new paper reports results, not included in the earlier paper, on the effect of the study’s “97% consensus msg” on subjects’ acceptance of climate change, their climate change risk perceptions, and their support for responsive policy measures.

The design of the study was admirably simple:

Administered to a group of 1,104 members of the US population, the experiment produced these results on the indicated attitudes:

Using pre and post measures from a national message test experiment, we found that all stated hypotheses were confirmed; increasing public perceptions of the scientific consensus causes a significant increase in the belief that climate change is (a) happening, (b) human-caused and (c) a worrisome problem. In turn, changes in these key beliefs lead to increased support for public action.

Using pre and post measures from a national message test experiment, we found that all stated hypotheses were confirmed; increasing public perceptions of the scientific consensus causes a significant increase in the belief that climate change is (a) happening, (b) human-caused and (c) a worrisome problem. In turn, changes in these key beliefs lead to increased support for public action.

I gotta say, I just don’t see any evidence in these results that the “97% consensus msg” meaningfully affected any of the outcome variables that the authors’ new writeup focuses on (belief in climate change, perceived risk, support for policy).

It’s hard to know exactly what to make of  the 0-100 “belief certainty” measures. They obviously aren’t as easy to interpret as items that ask whether the respondent believes in human-caused climate change, supports a carbon tax etc.

In fact, a reader could understandably mistake the “belief certainty” levels in the table as %’s of subjects who agreed with one or another concrete proposition. To find an explanation of what the “0-100” values are actually measurements of , one has to read the Climatic Change paper– or actually, the on-line supplementary information for the Climatic Change paper.

Weirdly, the authors simply don’t report how the information affected the proportion of subjects who said they believe in climate change, human-caused or otherwise! If the authors have data on %s who believed in climate change before & after etc, I’m sure readers would actually be more interested in those….

But based on the “belief certainty” values in the table, it looks to me like the members of this particular sample, were, on average, somewhere between ambivalent and moderately certain about these propositions before they got the “97% consensus msg.”

After, they got the message, I’d say they were, on average,  … somewhere between ambivalent and moderately certain about these propositions.

From “75.19” to “76.88” in “belief certainty”: yes, that’s “increased support for policy action,” but it sure doesn’t look like anything that would justify continuing to spend milions & millions of dollars on a social marketing campaign that has been more or less continuously in gear for over a decade with nothing but the partisan branding of climate science to show for it.

The authors repeatedly stress that the results are “statistically significant.”

But that’s definitely not a thing significant enough to warrant stressing.

Knowing that the difference between something and zero is “statistically significant” doesn’t tell you whether what’s being measured is of any practical consequence.

Indeed, w/ N = 1,104, even quantities that differ from zero by only a very small amount will be “statistically significant.”

The question is, What can we infer from the results, practically speaking?